Research On Fault Diagnosis Of Power Grid

One of the key technologies to realize intelligent scheduling is the realization of power system fault diagnosis. Fault diagnosis technology as the key part of smart grid dispatching decision support system can provide auxiliary decision for dispatch personnel. Thus the dispatchers can locate the fault and for the first time to the fault conditions to make a deal, so that the grid can quickly restore the normal operation of the state, prompting more intelligent power grid operation control. Therefore, the research of the fault diagnosis system has important practical significance and the mission of the times.With the rapid development of modern power system, and after the failure to achieve the complexity of diagnosis, making fault diagnosis system research at home and abroad are subject to attention, becomes a hot research topic in the field of current power.In this thesis, the fault diagnosis system, the first analysis of the currently system structure of the scheduling system of State Grid Company, focus on the aiarm information processing process in the presence of a number of issues, and in the light of these problems make improvement ideas. Then according to this train of thought established typical scheduling information model, dispatching center to upload scheduling information in accordance with the spacing of classification, and thus as the basis constructed with three layer properties of scheduling information data sets, designs based on frame network method the normal inference engine of the expert system in order to achieve the grid maintenance in the process of scheduling information adaptive shielding. Finally a hierarchy model of the scheduling information is established as a knowledge base of expert system divided into block storage, design the network knowledge reasoning process control scheme to realize the fault diagnosis of, and the simulation and analysis verify the diagnosis expert system in the reasoning process can save unnecessary search process, improve the diagnostic efficiency of retrieval.